Repetitive Processes Research Articles

NMPC‐Based Control of Underactuated Spacecraft: A Neighboring Extremal Approach Extended to SE(3)

ABSTRACTThe present work revolves around employing the benefits of neighboring extremal approach in the nonlinear model predictive control of mechanical systems evolving on SE(3). In the NMPC process, necessary conditions of optimality are extracted based on some discrete‐time version of the equations of motion referred as LGVI and the obtained TPBVP equations are solved using simplified sensitivity derivatives by means of an indirect shooting method. Taking into consideration that the repetitive optimization process of the NMPC is time consuming, making its implementation challenging, a method of neighboring extremal is proposed here for recalculating the responses of the systems in scenarios that face some unexpected changes in their parameters or are encountering some last‐minute re‐planning schemes. Based on the existing responses of the system to the first set of initial conditions, the reaction of the system to the altered set of initial conditions can be reconstructed without the need to solve the whole optimization process from scratch by utilizing the features of the NE method. A spacecraft model evolving on SE(3) with actuation constraints confirms the efficiency of the whole process in term of accuracy versus computation burden. Forasmuch as actuator failure is a probable yet important event in aerial/spatial missions, the performance of the control system in dealing with such situations is examined. The algorithm robustness and its capability to compensate the effects of the actuator loss is checked.

Why I Paint Thousands of Circles

Why I paint Thousands of Circles explores psychological barriers and multilayered themes that stem from a single horrific event that involved Moran’s father and his brother. The artist collates information, photos and constructs an ar(t)chaeological archive where family photos, product imagery, together with newspaper clips to form units of a historical and psychological mind map. The exposition becomes an auto-ethnographical exploration of mid 90's working class North West London. The repetitive painting process, exposed and documented in the exposition, functions as transformative method, where ambiguous feelings of a violent upbringing are directed towards the creation of a visual system with an inherent logic – “creating some kind of beauty out of ugliness.”

간호대학 교수의 이직의도 극복 경험

Objectives This study is to understand the experience of nursing professors overcoming turnover intention and to clarify the meaning and structure of the experience. Specifically, it is to derive a general structure by grasping from their perspective how nursing professors overcome the turnover situation. Methods This study is a qualitative study applying Giorgi(2009)'s phenomenological research method. The study participants are professors of nursing department who have worked as full-time professors for more than 5 years, and a total of 5 people understood the purpose of this study and voluntarily agreed to participate. Data collection was collected through individual in-depth interviews and observations from March 1, 2024 to May 18, 2024. The number of interviews for each participant was 2-4, and interviews were conducted at a place the participant wanted, and the time required for one interview was about 1 hour. Purposeful sampling was performed to select participants. The collected data went through a cyclical repetitive process of data collection and analysis based on the phenomenological research method developed by Giorgi(2009). Results The general structure of the experience of nursing professors overcoming the intention to turnover derived from the results of this study was ‘extreme situation that is difficult to endure’, ‘confirm reality’, ‘promoting stability’, ‘growth’, and ‘construction of place’, and a total of 5 upper themes, 8 lower themes, and 40 semantic units were derived. Conclusions Their experience was a series of processes in which they began to consider turnover through physical and psychological difficulties, confusion, and conflict while starting a nursing professor, but they identified their reality and promoted stability to build a position as their growth took place. Problems related to turnover of nursing professors are not limited to individual professors' problems, but it is necessary to carefully recognize experiences and difficulties at various levels. However, since coping with turnover is carried out on a personal level, it is desirable to seek systematic and concrete solutions at the formal level. This study walks the overall understanding of nursing professors who have overcome turnover by looking at the nursing professor's experience of overcoming turnover intention and provides evidence to understand the experience of nursing professors who have overcome turnover intention from a phenomenological point of view based on this result. It aims to contribute to improving the quality of nursing education for nursing students by allowing nursing professors to focus on nursing education without changing jobs.

Investigation of some issues of automation of elements of terminal complexes for transshipment of large-capacity containers

The topic of the work is the study of the reasons for increasing the efficiency of the use of marine container transportation in the entire supply chain. It is noted that the increase in their efficiency is explained by the possibility of transporting various types of cargo, the speed of handling vehicles, as well as the compactness of storage and safety of cargo. The problem of increasing ship capacity on the terms of shipping companies, which currently exists and is being solved by container terminal operators, has been formulated, which allows minimizing the time of ships parking in the port for processing, which in turn can be achieved by increasing the capacity of ships. Attention is drawn to the fact that reducing costs by increasing the efficiency of lifting and transport equipment stimulates investments in automation systems for almost all terminal operations, and in particular in lifting and transport equipment systems. It is emphasized that the automation process of container terminals differs depending on the task at hand (a new project is being considered or work is being carried out to automate an existing terminal). The implementation of automation at existing terminals is a more difficult task due to the problems of compatibility with the types of activities performed and resistance to ongoing changes, which primarily involve systematic and repetitive technological processes that can be programmed. Attention is drawn to the fact that container terminals use the latest operating systems to control and optimize the movement of containers inside the warehouse area (in particular, they are used at terminals where gantry cranes on a pneumatic (RTG) and on a rail (RMG) are used), and it is also emphasized that when working with RTG technology gantry cranes on a pneumatic vehicle are equipped with sensors connected to satellite positioning systems, which are located on the terminal area and on wheeled carts and are brought to the place of cargo operation by signals from the control room, at the same time, the dock operator controls only when lowering or lifting the container. Such information systems are also used on terminals operating using RMG technology. It is noted that automation of berthing container cranes is currently under development, since the complexity of its implementation lies in the fact that during pitching the vessel has a roll and a deflector, therefore, when moving it, it is difficult to determine the exact location of the container and the alignment of the spreader horizon with the container horizon, as a result, the guidance process? It requires additional time, which leads to a decrease in work productivity.

Optimization of Fuzzy Economic Order Quantity using Machine Learning and Weka

Inventory management is a crucial need for businesses because it requires a significant financial and human resource investment. Machine Learning models are used by e-commerce huge companies to manage their inventory to the demand for specific products. Businesses can benefit from inventory management as a service to increase sales and forecast product demand. Inventory Management might be revolutionised by AI and ML technologies, which would allow businesses to automate repetitive processes, make data-driven choices, and instantly optimise inventory levels. By analysing vast amounts of both historical and current data, these technologies can spot inclination, forecast, and recommend, all of which increase inventory accuracy and lower the chance of stockouts or overstocking. The most efficient solution is determined using the Kuhn-Tucker Method for Nonlinear Programming, which has an impact on the monthly cost. The proposed model employs the Signed distance approach for defuzzification and the Pentagonal fuzzy number to find the lowest cost and met the demand. To evaluate the inventory concept, purchased a dataset and calculated the profit and non profit using apriori algorithm and use the Weka software to analyse the average order quantity and total cost.

Risk Assessment of Employing Digital Robots in Process Automation

Using digital technologies is essential to gain a competitive advantage in the global market by adapting to new business models. While digital technologies make business processes efficient, they enable companies to make faster and more accurate decisions by automating daily and routine process tasks. Robotic process automation (RPA) automates routine and repetitive business processes, allowing many jobs performed by humans to be performed faster. This way, advantages such as reduced error rates, reduced costs, increased production speed, and labor productivity are provided. For the successful implementation of RPA, potential risks need to be considered. In this study, failure mode and effect analysis (FMEA) based on decomposed fuzzy sets (DFSs), a new extension of intuitionistic fuzzy sets, has been used to evaluate subjectiveness in expert judgments. Differing from the other extensions of fuzzy set theory, the advantage of DFSs is to simultaneously consider decision-makers’ optimistic and pessimistic answers. Thus, the answer given by the decision-maker to the positive and negative questions on the same subject defines the indeterminacy of the decision-maker, and the method takes this indeterminacy into account in the evaluation. This study assesses and evaluates the potential risks of six digital robots in process automation. Thirteen risks were individually assessed for each automated process. This study found “Sustainability challenge” critical in three processes, “Absence of governance management” in two, and “Security“ in one. Variability in risk importance arose from process vulnerabilities.

Exploring Journey Maps as Products From Qualitative Research: Application Through Food Insecure Veterans' Experiences.

Journey maps are graphic representations of participant, user, customer, or patient experiences or "journeys" with a particular phenomenon, product, business, or organization. Journey maps help visualize complex pathways and phases in accessible, digestible ways. They also capture emotions, reactions, and values associated with the processes participants undergo, complemented by images or quotes from participants. Here, we outline the foundations of journey maps in research and in practice settings. Our goal is to describe journey maps to researchers new to the product and emphasize the novelty and utility of journey maps as visual products from qualitative research particularly in a health setting. To explore journey maps-including their benefits, drawbacks, and relevance-we discuss examples including our own process for designing a journey map of food insecure Veterans' experiences using qualitative, in-depth interviews and supported by member checking. Our journey map depicts food insecurity as a repetitive process, a unique contribution given that many journey maps are designed with discrete starting and stopping points. We conclude by discussing the novelty of journey maps as innovative products that researchers can use to identify opportunities for process improvements and innovation using multiple data sources or methods.

Intelligent controllable ultrafast fiber laser via deep learning and adaptive optimization algorithm

Ultrafast fiber lasers based on nonlinear polarization rotation can generate femtosecond pulses with different pulse durations and high peak powers, which are powerful tools for engineering applications and scientific research. However, achieving a precise and repeatable polarization state for generating the ultrashort pulses with the shortest pulse duration remains a significant challenge. In this paper, we extend the use of recurrent neural networks and adaptive optimization algorithms, specifically designed to optimize repetitive processes in optical systems, to facilitate intelligent search and control aimed at achieving the minimum pulse duration within a mode-locked fiber laser cavity. Our multi-algorithm-based intelligent system can fully simulate and optimize the processes involved in hands-on experiments. Our intelligent system identified a mode-locked fiber laser with the shortest pulse duration of 465 fs, which was experimentally verified. The proposed intelligent algorithm not only identifies the shortest pulse but also holds significant potential for selecting related laser characteristic parameters. We believe this work opens up a novel avenue for exploration and optimization in mode-locked lasers and the intelligent laser can find practical applications in engineering and scientific research.

Adaptive search for broad attention based vision transformers

Vision Transformer (ViT) has prevailed among computer vision tasks for its powerful capability of image representation recently. Frustratingly, the manual design of efficient architectures for ViTs can be laborious, often involving repetitive trial and error processes. Furthermore, the exploration of lightweight ViTs remains limited, resulting in inferior performance compared to convolutional neural networks. To tackle these challenges, we propose Adaptive Search for Broad attention based Vision Transformers, called ASB, which automates the design of efficient ViT architectures by utilizing the broad search space and an adaptive evolutionary algorithm. The broad search space facilitates the exploration of a novel connection paradigm, enabling more comprehensive integration of attention information to improve ViT performance. Additionally, an efficient adaptive evolutionary algorithm is developed to efficiently explore architectures by dynamically learning the probability distribution of candidate operators. Our experimental results demonstrate that the adaptive evolution in ASB efficiently learns excellent lightweight models, achieving a 55% improvement in convergence speed over traditional evolutionary algorithms. Moreover, the effectiveness of ASB is validated across several visual tasks. For instance, on ImageNet classification, the searched model attains a performance of 77.8% with 6.5M parameters and outperforms state-of-the-art models, including EfficientNet and EfficientViT networks. On mobile COCO panoptic segmentation, our approach delivers 43.7% PQ. On mobile ADE20K semantic segmentation, our method attains 40.9% mIoU. The code and pre-trained models will be available soon in ASB-Code.

Atomic Nb-doping of WS2 for high-performance synaptic transistors in neuromorphic computing

Owing to the controllable growth and large-area synthesis for high-density integration, interest in employing atomically thin two-dimensional (2D) transition-metal dichalcogenides (TMDCs) for synaptic transistors is increasing. In particular, substitutional doping of 2D materials allows flexible modulation of material physical properties, facilitating precise control in defect engineering for eventual synaptic plasticity. In this study, to increase the switch ratio of synaptic transistors, we selectively performed experiments on WS2 and introduced niobium (Nb) atoms to serve as the channel material. The Nb atoms were substitutionally doped at the W sites, forming a uniform distribution across the entire flakes. The synaptic transistor devices exhibited an improved switch ratio of 103, 100 times larger than that of devices prepared with undoped WS2. The Nb atoms in WS2 play crucial roles in trapping and detrapping electrons. The modulation of channel conductivity achieved through the gate effectively simulates synaptic potentiation, inhibition, and repetitive learning processes. The Nb-WS2 synaptic transistor achieves 92.30% recognition accuracy on the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset after 125 training iterations. This study’s contribution extends to a pragmatic and accessible atomic doping methodology, elucidating the strategies underlying doping techniques for channel materials in synaptic transistors.

Digital automation and working life of HR practitioners: a gender analysis of the implications for workforce and work practices

Human Resources (HR) has emerged as a critical target for digital automation, aiming to reduce costs, improve quality, and expedite repetitive processes within organizations. Several literature reviews have explored this topic, yielding mixed results depending on factors related to the specific national and organizational context. They have highlighted issues such as lack of competence, safety and ethical dilemmas, and bias in decision-making processes. However, despite HR being predominantly female dominated, few studies have examined the gendered implications of digital automation in this field. Therefore, this paper aims to investigate the gendered implications of digital automation in recruitment processes and work practices within HR. Drawing upon gender theory, we will conduct a critical literature review based on existing reviews to reevaluate current knowledge. The findings suggest that while digital automation can enhance administrative efficiency in recruitment processes, it does not significantly impact the gender balance of the workforce. However, the findings also indicate that digital automation will likely transform the occupational role into a more masculinized way of working. This transformation is characterized by social distancing, technological rationality, and reliance on self-service systems. In conclusion, digital automation is poised to reshape the HR occupation into what we term “screen-level HR,” comprising a predominantly female workforce engaged in masculine coded work practices.

Repetition, persistence and generality: problematising the endurance of medieval urbanity

ABSTRACT It is proposed that an approach to difference through repetition, inspired by the writing of Gilles Deleuze, provides a conceptual approach to understand the endurance of urban life in medieval England. Perceptions of urban decline in later medieval England are contradicted by the persistence of urban places and communities. A tension, whereby persistence implies repetition yet decline implies discontinuity and difference, is evident. By developing a framework for understanding medieval urbanity as a series of repetitive processes of differentiation, this paper outlines how we might shift our approach to understanding past urban lives, with implications for how we engage with critical issues in urban studies, including the multiplicity, resilience and sustainability of urban lives.

Automatic device arrangement and wiring for fire alarm systems in metro stations: A case study

Fire alarm systems in metro stations play an important role in safeguarding lives and properties. The design process for these systems is complex and time-consuming due to the diversity of device types, wide distribution of device locations, and numerous compulsory design codes. This paper proposes an automatic design framework for fire alarm systems aimed at enhancing design efficiency. The framework includes five automated processes: information preprocessing, device arrangement, system loop generation, device wiring, and result generation. By converting the computer-aided design (CAD) results into building information models (BIM), building information and related professional device information are obtained, addressing the need for fire alarm system (FAS) design to integrate with multiple disciplines. The process can also automatically arrange FAS devices according to design specifications. Optimization algorithms, such as Ant Colony Algorithm and Prim Algorithm, are used to determine the device connection order and solve the problems of obstacle avoidance and orthogonal connections between two points. Compared to traditional design methods, the proposed automatic design framework for fire alarm systems in metro stations can not only satisfy the mandatory design requirements but also avoid the cumbersome and repetitive design process, providing new solutions for designers. Additionally, it can reduce wire length and save on construction costs. Using a real metro station as a case study, it is calculated that the automatic design can save between 8 % and 48 % of the wire length.

E-commerce on Hardware sales: A case study of Computer technologies Union in Owerri, Nigeria.

The incorporation of computers into contemporary company operations has significantly altered productivity and communication, essentially making paper-based file systems outdated. The advancement of technology has made it possible to manage data more accurately, streamline procedures, and obtain information more quickly. Better managerial skills are now a trademark of the digital age, allowing companies to accurately and quickly respond to information requests. Innovative business models have also been made possible by the transition to digital technology, especially in the areas of customer engagement and operational management. A more dynamic and responsive business environment has been made possible by the automation of repetitive processes, real-time data analysis, and enhanced communication channels. This has allowed organisations to respond to shifting market conditions with accuracy and agility. A significant advancement in this digital shift is the emergence of e-commerce platforms. These platforms have facilitated a smooth and mutually beneficial relationship between businesses and customers. Online shopping provides customers with an unparalleled level of ease, enabling them to peruse, evaluate, and buy things from the comfort of their own homes. These websites give businesses access to a wider market, an extra source of income, and insightful data analytics on consumer behaviour. By providing easy navigation, safe payment choices, and personalised experiences, the e-commerce model improves client engagement. The transition to e-commerce has increased revenues as well as customer happiness and brand loyalty by offering a convenient and effective purchasing experience. For Computer Technologies Union (CTU) in Owerri, the project's goal was to create an e-commerce website that would improve customer interaction and business operations in response to current trends. To enable simple online purchases, the website was built with a feature-rich catalogue of store merchandise and an intuitive shopping cart. The system was designed to be reliable, scalable, and effective by utilising a three-tier design. Although the middle layer, which used PHP, handled business logic with ease, the backend MySQL database ensured dependable data management. Shopping was simple and easy with the client interface, which could be accessed via a web browser. The speed and precision of service delivery at CTU Owerri were greatly enhanced by the new system after it was fully implemented. While the firm benefited from increased operational efficiency and the ability to better meet customer expectations, customers had a convenient and pleasurable shopping experience. In addition to modernising CTU Owerri's operations, this calculated decision gave it a competitive edge in the online market.

Prosecutor’s Supervision over the Implementation of Legislation on the Protection of Land and Soil

The article deals with the issue of prosecutorial supervision of compliance with legislation on the protection of land and soil. The author concludes that it is necessary to improve the system of state monitoring of soil conditions, rationing and accounting of the ecological state when using land plots, as well as assigning responsibilities for soil improvement to their users. The author emphasizes the need to develop a final position on the issues under consideration in reviews of the practice of application in the courts of the Russian Federation, federal legislation in the field of environmental protection, rational nature management. It is also necessary to regulate all possible repetitive processes for prosecutors during inspections in collections of methodological materials on the organization of prosecutorial supervision of compliance with legislation on land and soil protection.

Binder‐induced inorganic‐rich solid electrolyte interphase and physicochemical dual cross‐linked network for high‐performance SiOx anode

AbstractSilicon oxide (SiOx) is heralded as the forefront anode material for high‐energy density lithium‐ion batteries, owing to its exceptional specific capacity. Nevertheless, the traditional combination of polyacrylic acid binder and acetylene black conductive carbon continues to struggle with the immense stress induced by the repetitive volume expansion and contraction processes. Here we report a high ionic conductivity, sulfonyl fluoro‐containing binder for SiOx anode via free radical copolymerization reaction between perfluoro (4‐methyl‐3,6‐dioxaoct‐7‐ene) sulfonyl fluoride and acrylic acid. The electrode fabrication process incorporated amino‐functionalized carbon nanotubes (CNT‐NH2) as the conductive agent. A three‐dimensional conductive network structure is constructed through physical and chemical double cross‐linking interactions between the ‐COOH and ‐SO2F functional groups of PAF0.1 binder, the ‐NH2 groups of CNT‐NH2, and the ‐OH groups on the surface of SiOx, including hydrogen bonds and covalent bonds. In addition, the binder induces the formation of a solid electrolyte interphase (SEI) rich in inorganic components such as Li2O, Li2SO3, Li2CO3, and LiF. Benefiting from the synergistic effects of the physically and chemically double cross‐linked three‐dimensional conductive network constructed by the PAF0.1 binder and CNT‐NH2, coupled with the rich‐inorganic SEI, the SiOx anode delivers exceptional rate performance, cycle stability, and lithium‐ion diffusion dynamics.

An unsupervised embedding method based on streaming videos for process monitoring in repetitive production systems

Advanced production systems, such as multi-step assembly processes, predominantly comprise of repetitive operations. The repetitive manual or human–robot integrated production operations call for new real-time process management technologies such as the increasing use of sensors and the development of system intelligence. Conventional process monitoring and management methods, which are often labor-intensive, fall short in providing immediate and actionable insights. To tackle this limitation, we develop an unsupervised embedding method to automatically delineate the process into different stages and predict real-time progress information. We propose a Contrastive Variational Autoencoder as a feature extractor to adeptly embed repetitive processes into a Gaussian Mixture Model. Based on the extracted features, we propose an adaptive change-point detection and an Iterative Dynamic Time Wrapping algorithm to identify and segment multiple standardized process stages automatically. Theoretically, we establish the asymptotic optimality of the detected change-points associated with the given precision of image and feature extractors, ensuring the high-quality process stage separation and labeling. The proposed method autonomously extracts essential features encapsulating progress information from a limited set of unlabelled process videos. Through four diverse case studies including production of an actual aircraft spoiler, our method exhibits very promising performance. Specifically, it achieves an average of 98.14% accuracy in predicting production progress and 0.9202 area under the curve in predicting progress deviation across three distinct production environments. The proposed process monitoring method in repetitive production systems has the potential to significantly improve productivity, promote standardization of repetitive operations, and predict production deviations.

APPLICATION DEVELOPMENT IN PYTHON FOR CONCRETE MIX DESIGN ACCORDING TO SNI 7656-2012

Concrete mix design is an important aspect in concrete construction process as it determines the quality of produced concrete. In practice, the process of getting a suitable concrete mix for a certain project is repetitive process. For repetitive work, it is an advantage to be carried out by using an application. This study focuses on the development of the application that can used to design the concrete mixes. Python programming language was employed in the development because it offers easiness and supports a rapid application development. The work is started by studying the procedure outlined SNI 7656-2012. The pseudocode of the application was then developed and implemented in Python. Tkinter, the Python package, was used in the development of Graphical User Interface for the application. The application was successfully developed and tested to carry out the concrete mix design.

State Observer-Based Iterative Learning Control Design for Discrete Systems Using the Heavy Ball Method

The paper considers a state observer-based iterative learning control design problem for discrete linear systems. To accelerate the convergence of the learning error, a combination of the heavy ball method from optimization theory and the vector Lyapunov function method for a class of two-dimensional systems known as repetitive processes is used to develop a new design. A supporting numerical example is given, including a comparison with an existing design.

The Use of Orthoses Made With 3D Printer In Upper Extremity Rehabilitation: A Review

Recent advancements in technology have brought about a significant transformation in the healthcare industry, particularly in the rehabilitation services sector, with the adoption of 3D (3 Dimensional) printing. This technology plays a crucial role in designing customized, aesthetically pleasing, lightweight, and eco-friendly orthoses for upper extremity rehabilitation. 3D scanning systems accurately predict the production process of orthoses, making it easier to maintain records of orthotic models. These recorded models make repetitive and batch production processes more efficient and accessible. However, the use of 3D printers in developing countries is limited due to high costs. Additionally, there is a lack of evidence-based practices. To overcome these challenges, efforts are underway to improve software interfaces for orthotic production and integrate 3D printed orthoses into upper extremity rehabilitation models. Further research is necessary to explore the optimal parameters for 3D printing in upper extremity orthotic production across diverse demographics.